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Many years ago, I lost a painful amount on a major European corporation which was part of a basket of equities that a high-priced money manager had assembled for me. The AAA-rated corporation suddenly went bankrupt and it was 10% of my portfolio.
Never again, I said. That was 1999.
And so I began a two decade-long excavation into how to use technology to give my investments an edge, both in growth and capital preservation. My idea was to apply statistics to stock price series (and to fundamentals) in order to extract patterns of predictability. I joined up with a scientist in Cincinnati and a programmer in Singapore (who I had met online) and we got started.
Millions (literally, millions) of lines of codes were written. Hundreds of systems were imagined, designed and coded and tested by us.
We never lost money, nor did we make money.
We were stumped by all manner of challenges. Like our order being received on Nasdaq a mere 50 microseconds too late (compared to someone with a server just metres from the exchange). Like being bested by people who understood fast-trading bid-ask spreads better than we did. Like dark pools (don’t ask). Like being killed at the open auctions, where prices bounce wildly and without discernible direction for about 15 seconds.
No matter. I got into this so deeply and spent so much time reading and testing and coding that I can now speak with callused-knuckle experience about the tech-driven investment landscape.
Here is the view:
The application of technology to investing happens in three broad areas. These are trading, company analysis and asset allocation. There are other related tech developments like cryptocurrencies, but that is a subset for another day.
1. Trading
Trading refers to the activity of taking a bid price for an asset from a buyer and matching it as closely as possible to an ask price from a seller. The transaction is handled by a broker – a human, historically, but increasingly via a computer algorithm. Trades are also assembled and codified and repackaged into a moving price chart, so familiar to all of us in stock market charts.
Over the last 20 years the advent of real-time ticker price feeds, powerful low-cost computing and fast fibre networks has given the ability to both the 16-year-old enthusiast and the PhDs of Goldman Sachs to ingest these charts and to feed them into algorithms with the hope of predicting where the price will go next.
This may be from the micro-second timeframe all the way to much larger frames – minutes, days, months, even years. And the algorithms? Design or buy, it really doesn’t matter to the computer.
How well have these trading algorithms fared? In the early days of technical trading (around 1980s) it was indeed possible to find what is called “market inefficiencies” to gain an edge – sometimes a significant one. History is replete with sudden wealth fuelled by a smart trading algorithm.
But nothing lasts forever, and as new entrants started diving into algo-trading marketing, opportunities for an edge became harder to find, particularly with the advent of high-frequency trading and machine learning.
There are hundreds of books on technical trading (sometimes called technical analysis). I have read many. Oscillators, head-and-shoulders stochastics, cyclicals, candle patterns, moving average breaches, etc. A few seconds of analysis will reveal the obvious – if there was a way to beat a market with these published techniques, then it would be either quashed by hoards immediately, or everyone would be billionaires. In my view, it is all nonsense.
But lurking in the millions of times series charts (of which there are millions – stock prices, bond yields, futures, interest rates, unemployment, agricultural figures, weather forecasts) there may well be gold. Sooner or later someone (or an algorithm) who finds a short-to-long-term price predictor will get rich if they (or their algorithm) are lucky enough to move fast, ahead of the mob. Or at least faster than a competitive algorithm... For a short period before the window closes.
2. Company analysis
Compared to trading, the world of company analysis is more staid, and has been around for long before tech came on the scene. Ask Warren Buffett.
Take a company’s published financial figures and apply some math to the various rows and columns in the income/balance sheet and cash flow statements. (This usually refers to public companies where such figures are publicly accessible.) It can obviously be done faster and deeper with tech now, and more companies and other variables (like sector analyses) can be cross-correlated as part of the analysis.
Also central to company analysis is valuation. How much is a company worth? Does the invisible hand of the market know? Or a clever human analyst in a corner office somewhere? Or a smarter piece of software in the cloud?
Take a look at the valuations of some of today’s unicorns. Like WeWork (estimated at $4bn in August). I certainly gasp, failing to understand some of these valuations. But it is likely that various pieces of smart software disagree with me.
The analysis of a company’s prospects has, of course, always been part art. Is the CEO a good enough leader? Will the marketing plans strike a nerve with customers? Are there unseen political risks in geographies of consumption? Is the IP defensible (remember MXit?).
These factors are currently beyond tech analysis. But then again: never say never.
3. Asset allocation
Asset allocation has also been around for a long time, both within an asset class (like, for example, property stocks vs tech stocks, agri stocks or manufacturing stocks), and across asset classes (like stocks vs bonds vs collectibles vs currencies, etc).
This is a massive area for tech investment right now. Companies like Betterment provide to the retail investor the ability to balance a portfolio according to the investor’s needs – it could, for example, exclude stocks with exposure to alcohol or cigarettes, and increase exposure to renewables (with a little biotech on the side) at a fraction of the cost of a human analyst or broker.
Technology addressing this fecund ground can instantly repurpose a portfolio based on user-defined rules, instantaneously adjusting for risk and liquidity and other measures. While tech has been applied to allocation since before the days of Harry Markowitz’s portfolio allocation theories of the 1970s, it has only recently become available to retail investors via low-cost investment applications, for which there is vicious competition.
The sea-change in investor tech has been driven by a single phenomenon – Moore’s Law. While this originally was written to predict the density of transistors on a chip, it has been somewhat bastardised to extend to storage, communications speeds, saturation of smartphones, etc. It has had the effect of exploding out the black-box secrets living behind the doors of the grand old investment houses; and into the hands of the common investor.
And it is just started. In the chase for alpha (a real definable word which basically means outsize profits) in a world in which our old assumptions (like always increasing property prices, currency stability, the robustness of derivatives, predictable stock market volatility etc) are being shot down every few years, we can be sure that technology (and particularly the promise of AI and machine learning) will fuel an arms race for the smartest investment strategies.
It is not clear who will win, or whether it is winnable, or whether the underreported wisdom of simply investing in the S&P 500 will calmly take the prize.
Finally, what does this all mean for the average consumer, who simply wants the best return for his or her money? It means homework. Handing over cash for investment to an institution or software package or fund manager is a consequential decision, and is much more complex today than a generation ago. It means familiarising oneself with the latest in investment technologies, even on a superficial basis. It means a lot of Googling, and even more scepticism.
If the individual consumer is not prepared to do this, well, caveat emptor.
Steven Boykey Sidley is a director at Bridge Capital Future Advisory.
This article forms part of finweek’s Collective Insight series titled “How technology is impacting on financial decision-making” published in the 24 October 2019 edition of finweek. To read the entire series, get a copy of this magazine here.
